Automating insect monitoring using unsupervised near-infrared sensors

TL;DR

This paper introduces a novel wireless near-infrared sensor network for autonomous, long-term insect monitoring, providing more detailed and frequent data than traditional trapping methods, aiding biodiversity and agricultural management.

Contribution

The study presents a new unsupervised infrared sensor system capable of continuous insect monitoring, with validated correlation to traditional trapping methods and enhanced data collection.

Findings

Sensors recorded 19 times more insect observations than traps.

The sensor data showed a significant correlation (r=0.61) with trap counts.

The system operates autonomously over weather and seasons.

Abstract

Insect monitoring is critical to improve our understanding and ability to preserve and restore biodiversity, sustainably produce crops, and reduce vectors of human and livestock disease. However, conventional monitoring methods of trapping and identification are time consuming and thus expensive. Here, we present a network of distributed wireless sensors, recording backscattered near-infrared modulation signatures from insects. The instrument is a compact sensor based on dual-wavelength infrared light emitting diodes and is capable of unsupervised, autonomous long-term insect monitoring over weather and seasons. The sensor records the backscattered light at kHz pace from each insect transiting the measurement volume. Insect observations are automatically extracted and transmitted with environmental metadata over cellular connection to a cloud-based database. The recorded features include wing beat harmonics, melanisation and flight direction. To validate the sensor's capabilities, we tested the correlation between daily insect counts from an oil seed rape field measured with six yellow water traps and six sensors during a 4-week period. A comparison of the methods found a Spearman's rank correlation coefficient of 0.61 and a p-value of 0.0065, with the sensors recording approximately 19 times more insect observations and demonstrating a larger temporal dynamic than conventional trapping.